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At least 847 records · Page 47

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗

A small-molecule fragment that emulates binding of receptor and broadly neutralizing antibodies to influenza A hemagglutinin

The influenza virus hemagglutinin (HA) glycoprotein mediates receptor binding and membrane fusion during viral entry in host cells. Blocking these key steps in viral infection has applications for development of novel antiinfluenza therapeutics as well as vaccines. However, the lack of structural information on how small molecules can gain a foothold in the small, shallow receptor-binding site (RBS) has hindered drug design against this important target on the viral pathogen. Here, we report on the serendipitous crystallization-based discovery of a small-molecule N -cyclohexyltaurine, commonly known as the buffering agent CHES, that is able to bind to both group-1 and group-2 HAs of influenza A viruses. X-ray structural characterization of group-1 H5N1 A/Vietnam/1203/2004 (H5/Viet) and group-2 H3N2 A/Hong Kong/1/1968 (H3/HK68) HAs at 2.0-Å and 2.57-Å resolution, respectively, revealed that N -cyclohexyltaurine binds to the heart of the conserved HA RBS. N -cyclohexyltaurine mimics the binding mode of the natural receptor sialic acid and RBS-targeting bnAbs through formation of similar hydrogen bonds and CH-π interactions with the HA. In H3/HK68, N -cyclohexyltaurine also binds to a conserved pocket in the stem region, thereby exhibiting a dual-binding mode in group-2 HAs. We conclude these long-awaited structural insights into RBS recognition by a noncarbohydrate-based small molecule enhance our knowledge of how to target this important functional site and can serve as a template to guide the development of novel broad-spectrum small-molecule therapeutics against influenza virus.

60 APPLIED LIFE SCIENCES↗

Interoperability of Materials Database Systems in Support of Nuclear Energy Development and Potential Applications for Fuel Cell Material Selection

Materials database interoperability has been of great interest in recent years for information exchange in support of research and development (R&D). In response to data and knowledge sharing needs of the GenIV International Forum (GIF) for global collaboration in nuclear energy R&D, the European Commission JRC Institute for Energy and Transport (JRC-IET) and the Oak Ridge National Laboratory (ORNL) have established a materials database interoperability project that develops techniques for automated materials data exchange between systems hosted at the two institutes MatDB Online at JRC IET and the Gen IV Materials Handbook at ORNL, respectively. The work to enable automated exchange of data between the two systems leverages the XML data import and export functionalities of both systems in combination with recently developed standards for engineering materials data. The preliminary results of data communication between the two systems have demonstrated the feasibility and efficiency of materials database interoperability, which constructs an interoperation framework that can be seamlessly integrated into the high-throughput First Principles material databases and thus advance the discovery of novel materials in fuel cell applications.

36 MATERIALS SCIENCE↗

Durable and High-Performance SOECs Based on Proton Conductors for Hydrogen Production

Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).

08 HYDROGEN↗

Fermi/GBM Results of Magnetars

Magnetars are magnetically powered rotating neutron stars with extreme magnetic fields (over 10(exp 14) Gauss). They were discovered in the X- and gamma-rays where they predominantly emit their radiation. Very few sources (roughly 18) have been found since their discovery in 1987. NASA's Fermi Gamma-ray Space Telescope was launched June 11,2009; since then the Fermi Gamma-ray Burst Monitor (GBM) recorded emission from four magnetar sources. Two of these were brand new sources, SGR J0501 +4516, discovered with Swift and extensively monitored with Swift and GBM, SGR J0418+5729, discovered with GBM and the Interplanetary Network (IPN). A third was SGR Jl550-5418, a source originally classified as an Anomalous X-ray Pulsar (AXP IEI547.0-5408), but exhibiting a very prolific outburst with over 400 events recorded in January 2009. In my talk I will give a short history of magnetars and describe how this, once relatively esoteric field, has emerged as a link between several astrophysical areas including Gamma-Ray Bursts. Finally, I will describe the exciting new results of Fermi in this field and the current status of our knowledge of the magnetar population properties and magnetic fields.

Kouveliotou, chryssa↗

Bias correction factors for near-Earth asteroids

Knowledge of the population size and physical characteristics (albedo, size, and rotation rate) of near-Earth asteroids (NEA's) is biased by observational selection effects which are functions of the population's intrinsic properties and the size of the telescope, detector sensitivity, and search strategy used. The NEA population is modeled in terms of orbital and physical elements: a, e, i, omega, Omega, M, albedo, and diameter, and an asteroid search program is simulated using actual telescope pointings of right ascension, declination, date, and time. The position of each object in the model population is calculated at the date and time of each telescope pointing. The program tests to see if that object is within the field of view (FOV = 8.75 degrees) of the telescope and above the limiting magnitude (V = +1.65) of the film. The effect of the starting population on the outcome of the simulation's discoveries is compared to the actual discoveries in order to define a most probable starting population.

Benedix, Gretchen K.↗

The Extreme Case of Magnetars

Magnetars are magnetically powered rotating neutron stars with extreme magnetic fields (over 10(exp 14) Gauss). They were discovered in the X- and gamma-rays where they predominantly emit their radiation. Very few sources (roughly 18) have been found since their discovery in 1987. NASA's Fermi Gamma-ray Space Telescope was launched June 11, 2009; since then the Fermi Gamma-ray Burst Monitor (GBM) recorded emission from four magnetar sources. Two of these were brand new sources, SGR J0501+4516, discovered with Swift and extensively monitored with Swift and GBM, SGR J0418+5729, discovered with GBM and the Interplanetary Network (IPN). A third was SGR J1550-5418, a source originally classified as an Anomalous X-ray Pulsar (AXP 1E1547.0-5408), but exhibiting a very prolific outburst with over 400 events recorded in January 2009. In my talk I will give a short history of magnetars and describe how this, once relatively esoteric field, has emerged as a link between several astrophysical areas including Gamma-Ray Bursts. Finally, I will describe the exciting new results of Fermi in this field and the current status of our knowledge of the magnetar population properties and magnetic fields.

Kouveliotou, Chryssa↗

On the data-driven inference of modulatory networks in climate science: An application to West African rainfall

Decades of hypothesis-driven and/or first-principles research have been applied towards the discovery and explanation of the mechanisms that drive climate phenomena, such as western African Sahel summer rainfall~variability. Although connections between various climate factors have been theorized, not all of the key relationships are fully understood. We propose a data-driven approach to identify candidate players in this climate system, which can help explain underlying mechanisms and/or even suggest new relationships, to facilitate building a more comprehensive and predictive model of the modulatory relationships influencing a climate phenomenon of interest. We applied coupled heterogeneous association rule mining (CHARM), Lasso multivariate regression, and dynamic Bayesian networks to find relationships within a complex system, and explored means with which to obtain a consensus result from the application of such varied methodologies. Using this fusion of approaches, we identified relationships among climate factors that modulate Sahel rainfall. As a result, these relationships fall into two categories: well-known associations from prior climate knowledge, such as the relationship with the El Niño–Southern Oscillation (ENSO) and putative links, such as North Atlantic Oscillation, that invite further research.

54 ENVIRONMENTAL SCIENCES↗

The PTPN2/PTPN1 inhibitor ABBV-CLS-484 unleashes potent anti-tumour immunity

Immune checkpoint blockade is effective for some patients with cancer, but most are refractory to current immunotherapies and new approaches are needed to overcome resistance. The protein tyrosine phosphatases PTPN2 and PTPN1 are central regulators of inflammation, and their genetic deletion in either tumour cells or immune cells promotes anti-tumour immunity. However, phosphatases are challenging drug targets; in particular, the active site has been considered undruggable. Here we present the discovery and characterization of ABBV-CLS-484 (AC484), a first-in-class, orally bioavailable, potent PTPN2 and PTPN1 active-site inhibitor. AC484 treatment in vitro amplifies the response to interferon and promotes the activation and function of several immune cell subsets. In mouse models of cancer resistant to PD-1 blockade, AC484 monotherapy generates potent anti-tumour immunity. We show that AC484 inflames the tumour microenvironment and promotes natural killer cell and CD8 + T cell function by enhancing JAK–STAT signalling and reducing T cell dysfunction. Inhibitors of PTPN2 and PTPN1 offer a promising new strategy for cancer immunotherapy and are currently being evaluated in patients with advanced solid tumours (ClinicalTrials.gov identifier NCT04777994). More broadly, our study shows that small-molecule inhibitors of key intracellular immune regulators can achieve efficacy comparable to or exceeding that of antibody-based immune checkpoint blockade in preclinical models. Finally, to our knowledge, AC484 represents the first active-site phosphatase inhibitor to enter clinical evaluation for cancer immunotherapy and may pave the way for additional therapeutics that target this important class of enzymes.

60 APPLIED LIFE SCIENCES↗

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗

A metabolic pathway for catabolizing levulinic acid in bacteria

Microorganisms can catabolize a wide range of organic compounds and therefore have the potential to perform many industrially relevant bioconversions. One barrier to realizing the potential of biorefining strategies lies in our incomplete knowledge of metabolic pathways, including those that can be used to assimilate naturally abundant or easily generated feedstocks. For instance, levulinic acid (LA) is a carbon source that is readily obtainable as a dehydration product of lignocellulosic biomass and can serve as the sole carbon source for some bacteria. Yet, the genetics and structure of LA catabolism have remained unknown. Here, we report the identification and characterization of a seven-gene operon that enables LA catabolism in Pseudomonas putida KT2440. When the pathway was reconstituted with purified proteins, we observed the formation of four acyl-CoA intermediates, including a unique 4-phosphovaleryl-CoA and the previously observed 3-hydroxyvaleryl-CoA product. Using adaptive evolution, we obtained a mutant of Escherichia coli LS5218 with functional deletions of fadE and atoC that was capable of robust growth on LA when it expressed the five enzymes from the P. putida operon. Here, this discovery will enable more efficient use of biomass hydrolysates and metabolic engineering to develop bioconversions using LA as a feedstock.

59 BASIC BIOLOGICAL SCIENCES↗

Basic Research Needs for Communicating Basic Science

Basic science—also known as discovery or foundational research—is the fuel that powers our innovation ecosystem and the global economy. Despite this critical role, public awareness for how and where basic research is done, how it is funded, and its need for sustainable support over the long term is negligible. The ability to engage broader publics in awareness and understanding of basic science is paramount if we are to maintain the research enterprise that has transformed the modern world. Communicating how the federal government stewards taxpayer dollars for basic science is also incumbent on the agencies and scientists who use public dollars for their research. However, science communication is a complex activity that requires advanced planning, resources, and skill. Therefore, it is important to understand what we know about communicating basic science to the broader publics, as well as what we do not know, to be strategic and better equipped for effective, meaningful engagement. To identify and explore knowledge gaps in our understanding of basic science communication, the Science Public Engagement Partnership (SciPEP), a public-private partnership of the U.S. Department of Energy (DOE) Office of Science and The Kavli Foundation, carried out a two-part landscape analysis: literature surveys of social science scholarship and a basic research needs conference.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Magnetic, electrical and structural properties of the Re-doped ruthenocuprate Ru{sub 1−x}Re{sub x}Sr{sub 2}GdCu{sub 2}O{sub y}

Highlights: • We investigated the effect of the dilution of magnetic Ru sub-lattice of RuSr{sub 2}GdCu{sub 2}O{sub 8}. • We synthesized the doped compound Rui{sub x}Re{sub x}Sr{sub 2}GdCu{sub 2}O{sub y}, for 3%, 6%, 9% and 12% Re. • Re would affect the electron coupling: just 3 and 6% samples were superconductor. • Superconductivity emergence strongly affects magnetic properties of 3 and 6% samples. • A weak ferromagnetic component is consistent with a globally antiferromagnetic system. - Abstract: Despite the discovery of new superconductors classes, high-Tc oxides continue to be a current topic, because of their complex phase diagrams and doping-dependant effects (allowing one to investigate the interaction between orbitals), as well as structural properties such as lattice distortion and charge ordering, among many others. Ruthenocuprates are magnetic superconductors in which the magnetic transition temperature is much higher than the critical superconducting temperature, making them unique compounds. With the aim of investigating the dilution of the magnetic Ru sub-lattice, we proposed the synthesis of the Ru{sub 1−x}Re{sub x}Sr{sub 2}GdCu{sub 2}O{sub y} ruthenocuprate-type family, adapting the known two-step process (double perovskite + CuO) by directly doping the double perovskite, thus obtaining the perovskite compound Sr{sub 2}GdRu{sub 1−x}Re{sub x}O{sub y}, which represents a new synthesis process to the best of our knowledge. Our samples were structurally characterized through X-ray diffraction, and the patterns were analysed via Rietveld refinement. A complete magnetic characterization as a function of temperature and applied field, as well as transport measurements were carried out. We discuss our results in the light of the two-lattice model for ruthenocuprates, and a relation between RuO{sub 2} (magnetic) and CuO{sub 2} (superconductor) sub-lattices can clearly be observed.

36 MATERIALS SCIENCE↗

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. Here, we describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

60 APPLIED LIFE SCIENCES↗

The Most Distant H i Galaxies Discovered by the 500 m Dish FAST

Abstract Neutral hydrogen (Hi) is the primary component of the cool interstellar medium (ISM) and is the reservoir of fuel for star formation. Owing to the sensitivity of existing radio telescopes, our understanding of the evolution of the ISM in galaxies remains limited, as it is based on only a few hundred galaxies detected in Hibeyond the local Universe. With the high sensitivity of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we carried out a blind Hisearch, the FAST Ultra-Deep Survey, which extends to redshifts up to 0.42 and a sensitivity of 50μJy beam −1 . Here, we report the first discovery of six galaxies in Hi atz> 0.38. For these galaxies, the FAST angular resolution of ∼4′ corresponds to a mean linear size of ∼ 1.3 h 70 − 1 Mpc. These galaxies are among the most distant Hiemission detections known, with one having the most massive Hicontent ( 10 10.93 ± 0.04 h 70 − 2 M ⊙ ). Using recent data from the DESI survey and new observations with the Hale, Big Telescope Alt-azimuth, and Keck telescopes, optical counterparts are detected for all galaxies within the 3σpositional uncertainty ( 0.5 h 70 − 1 Mpc) and 200 km s −1 in recession velocity. Assuming that the dominant source of Hiis the identified optical counterpart, we find evidence of evolution in the Hicontent of galaxies over the last 4.2 Gyr. Our new high-redshift Higalaxy sample provides the opportunity to better investigate the evolution of cool gas in galaxies. A larger sample size in the future will allow us to refine our knowledge of the formation and evolution of galaxies.

Astronomy & Astrophysics↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

Accelerated Search for BaTiO 3 -Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design

The problem that is considered is that of maximizing the energy storage density of Pb-free BaTiO3-based dielectrics at low electric fields. It is demonstrated that how varying the size of the combinatorial search space influences the efficiency of material discovery by comparing the performance of two machine learning based approaches where different levels of physical insights are involved. It is started with physics intuition to provide guiding principles to find better performers lying in the crossover region in the composition–temperature phase diagram between the ferroelectric phase and relaxor ferroelectric phase. Such an approach is limiting for multidopant solid solutions and motivates the use of two data-driven machine learning and design strategies with a feedback loop to experiments. Strategy I considers learning and property prediction on all the compounds, and strategy II learns to preselect compounds in the crossover region on which prediction is carried out. By performing only two active learning loops via strategy II, the compound (Ba0.86Ca0.14)(Ti0.79Zr0.11Hf0.10)O3 is synthesized with the largest energy storage density ≈73 mJ cm-3 at a field of 20 kV cm-1 , and an insight into the relative performance of the strategies using varying levels of knowledge is provided.

36 MATERIALS SCIENCE↗

Heterogeneous processes: Laboratory, field, and modeling studies

The efficiencies of chemical families such as ClO(x) and NO(x) for altering the total abundance and distribution of stratospheric ozone are controlled by a partitioning between reactive (active) and nonreactive (reservoir) compounds within each family. Gas phase thermodynamics, photochemistry, and kinetics would dictate, for example, that only about 1 percent of the chlorine resident in the lower stratosphere would be in the form of active Cl or ClO, the remainder existing in the reservoir compounds HCl and ClONO2. The consistency of this picture was recently challenged by the recognition that important chemical transformations take place on polar regions: the Airborne Antarctic Ozone Experiment (AAOE) and the Airborne Arctic Stratospheric Expedition (AASA). Following the discovery of the Antarctic ozone hole, Solomon et al. suggested that the heterogeneous chemical reaction: ClONO2(g)+HCl(s) yields Cl2(g)+HNO3(s) could play a key role in converting chlorine from inactive forms into a species (Cl2) that would rapidly dissociate in sunlight to liberate atomic chlorine and initiate ozone depletion. The symbols (s) and (g) denote solid phase, or adsorbed onto a solid surface, and gas phase, respectively, and represent the approach by which such a reaction is modeled rather than the microscopic details of the reaction. The reaction was expected to be most important at altitudes where PSC's were most prevalent (10 to 25 km), thereby extending the altitude range over which chlorine compounds can efficiently destroy ozone from the 35 to 45 km region (where concentrations of active chlorine are usually highest) to lower altitudes where the ozone concentration is at its peak. This chapter will briefly review the current state of knowledge of heterogeneous processes in the stratosphere, emphasizing those results obtained since the World Meteorological Organization (WMO) conference. Sections are included on laboratory investigations of heterogeneous reactions, the characteristics and climatology of PSC's, stratospheric sulfate aerosols, and evidence of heterogeneous chemical processing.

Poole, Lamont R.↗